Abstract: An evaluation unit is fed with driving-related diagnostic data of a reference railway vehicle. The diagnostic data contain driving-related properties, on which a parameter of the wheel of the reference railway vehicle depends. The evaluation unit is also fed with wheel measurement data with measured values of the parameter of the wheel of the reference railway vehicle. The evaluation unit determines a function between the driving-related diagnostic data and the wheel measurement data via a supervised learning algorithm. The evaluation unit is fed with driving-related diagnostic data containing driving-related properties of an observed railway vehicle, on which a parameter of the wheel of the observed railway vehicle depends. The parameter of the wheel of the observed railway vehicle is determined by the evaluation unit using the determined function and the diagnostic data of the observed railway vehicle.

Abstract: A method analyzes conditions of technical components in view of a rarity and/or an abnormality of a condition. To provide a reliable analysis and thus a safely operating system the method includes: a) describing conditions of the technical components in a behavioral input space that is spanned by state variables, which are characteristic for the technical components, b) analyzing a condition of one technical component in respect to other conditions of this technical component in the behavioral input space, whereby a rarity of this condition of the technical component is detectable, and c) analyzing the condition of the technical component also in respect to analyses of conditions of further technical components in the behavioral input space. Whereby an abnormality of the condition of the technical component is detectable.

Abstract: A method for energy-optimized operation of a rail vehicle fleet. The fleet includes n rail vehicles, each with a state-influencing system for influencing a vehicle state to generate and/or consume electrical energy and a computer unit trained by machine learning. For every i of 1 to n during operation of the rail vehicle fleet an action to be applied to the state-influencing system of the i-th rail vehicle is selected by the computer unit of the i-th rail vehicle while taking into account at least one target criterion for the i-th rail vehicle and according to vehicle, location, and/or route-related status parameters. The action, when applied to the state-influencing system of the i-th rail vehicle, contributes to the optimization of an electrical total energy balance of the state-influencing systems of the rail vehicle fleet, and the selected action is applied to the state-influencing system of the i-th rail vehicle.

Abstract: A method for determining a lane change indication of a vehicle, preferably a passenger car, includes the following steps: loading a lane change model and position data of the vehicle, the position data indicating a distance of the vehicle from a lane center of a lane; and determining a lane change indication using the position data and the lane change model.

Abstract: An accurate and reliable maintenance method for controlling a condition of a wear and tear element of a track-bound vehicle includes determining a second condition of the element, being a chronological subsequent condition to a first condition, by starting from the first condition of the element, by using a machine learning algorithm, representing a chronological behavior of the element. A first action performable on the element is determined by using the determined second condition of the element and at least one predefined conditional criterion for the element. A resulting third condition of the element is determined by using a conditional change of the element. The conditional change is a consequence of the first performable action. A second performable action performable on the element is determined one more time by using the resulting third condition of the element and at least one predefined conditional criterion for the element.

Abstract: An evaluation unit is fed with driving-related diagnostic data of a reference railway vehicle. The diagnostic data contain driving-related properties, on which a parameter of the wheel of the reference railway vehicle depends. The evaluation unit is also fed with wheel measurement data with measured values of the parameter of the wheel of the reference railway vehicle. The evaluation unit determines a function between the driving-related diagnostic data and the wheel measurement data via a supervised learning algorithm. The evaluation unit is fed with driving-related diagnostic data containing driving-related properties of an observed railway vehicle, on which a parameter of the wheel of the observed railway vehicle depends. The parameter of the wheel of the observed railway vehicle is determined by the evaluation unit using the determined function and the diagnostic data of the observed railway vehicle.